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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： Teaching

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Sensor-Based Authentication on Smartphones via Integrating Auxiliary Information

Sensor-Based Authentication on Smartphones via Integrating A...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yingjie Wang Ruimin Hu National Engineering Research Center for Multimedia Software Wuhan University Whuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Whuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the widespread use of smartphones, more and more private information is stored on the phone, and the loss or theft of the phone can lead to data leakage, theft of property, and other problems. Traditional active authentication methods based on passwords, faces, fingerprints, etc. authenticate only once at login, which still has some hidden dangers. Authentication methods based on behavioral biometrics such as walking gait, touch screen, keystroke, etc. can provide implicit and continuous authentication services, and thus have been widely studied recently. Considering the limited computational resources of smart devices, we first introduce the state-space model with linear model complexity, i.e., Mamba, to extract deep feature representations from raw signal sequences in the sensor-based authentication task. Then a series of auxiliary information from different domains are proposed and fused to the deep features to enhance the consistent semantic information and ultimately improve the discriminative ability of the model. Our model A-Mamba, i.e., Auxiliary-Mamba, is experimented on two public datasets. The experimental results show that our model outperforms existing approaches.

关键词： Biological system modeling Semantics Authentication Passwords Touch sensitive screens Feature extraction State-space methods Smart devices Smart phones Spectral analysis

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chiming Duan Tong Jia Yong Yang Guiyang Liu Jinbu Liu Huxing Zhang Qi Zhou Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%.

关键词： Training Industries Systems operation Large language models Retrieval augmented generation Active learning Noise software systems Speech processing Anomaly detection

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Research on Optimization of Nuclear Power Security DCS Gateway Encryption Communication Based on OPC UA Protocol 9th

Research on Optimization of Nuclear Power Security DCS Gatew...

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9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Zhou, Pengjun Chen, Zhi Jiang, Wei Liu, Jie Zhang, Yan Zhong, Ke Wu, Zhiqiang School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (纸本)9783031302329

Nuclear power safety grade DCS gateway is an important hub for information transmission between safety grade equipment and non-safety grade equipment, and its security is very important in the transmission process. At present, the communication protocol used by the Chinese nuclear power security grade DCS gateway does not use data encryption in the data transmission process. However, the signature certificate, user authentication, access control and data encryption transmission of OPC UA protocol have more advantages in ensuring the data security of nuclear power security grade DCS gateway, which helps to meet the technical requirements for nuclear power security grade DCS information security. Based on the cryptography theory, this paper analyzes the security and efficiency of the original RSA encryption algorithm of the OPC UA protocol, and concludes that the encryption efficiency of the RSA algorithm is poor, which leads to the application of the OPC UA protocol to the nuclear power security grade DCS gateway communication process can not meet the time requirements of data transmission, so it is necessary to optimize and improve the communication process of the OPC UA protocol applied to the nuclear power security grade DCS gateway. This paper optimizes and improves the encryption transmission in the communication process of the nuclear power security grade DCS gateway of OPC UA protocol, and proposes a hybrid encryption algorithm of SM2 and AES to replace the original encryption method in OPC UA protocol. Through experiments, it proves that the communication optimization method improves the communication efficiency of the original protocol, and achieves the efficiency index of nuclear power security grade DCS gateway for data transmission. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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An Innovative Study on the Detection of Foreign Objects in Electric Wires

An Innovative Study on the Detection of Foreign Objects in E...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Fangyu Hu Haoyu Sun Junhao Zhang Yuhui Li School of Software Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

With the development of modern society, the demand for electricity consumption is also gradually increasing, timely power inspection, the removal of foreign bodies at the power line is very important. Aiming at the problems that manual inspection consumes a lot of human resources, time and low efficiency, and aiming at the problems of poor performance and low accuracy of target detection model, this paper proposes an automatic power line foreign body detection method based on YOLOv5-6.0 target detection model. In order to extract features better, the C3 module in the model is changed to CBAMC3 module which introduces CBAM attention mechanism. Meanwhile, the performance of the YOLOv5-6.0 model before and after the improvement in training and foreign body detection was compared in this paper to prove the effectiveness of the improvement.

关键词： YOLO Training Adaptation models Accuracy Wires Transfer learning Inspection

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SYSTEMATIC LITERATURE REVIEW ON APPLICATION OF LEARNING-BASED APPROACHES IN CONTINUOUS INTEGRATION

arXiv

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arXiv 2024年

作者： Arani, Ali Kazemi Le, Triet Huynh Minh Zahedi, Mansooreh Babar, M. Ali CREST-The Centre for Research on Engineering Software Technologies University of Adelaide AdelaideSA5005 Australia School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI. © 2024, CC BY.

关键词： Automation

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How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild 31

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhang, Heng Huang, Shaoyuan Xu, Mengwei Guo, Deke Wang, Xiaofei Leung, Victor C.M. Wang, Wenyu College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada Co. Ltd Shanghai China

ISBN: (纸本)9798350399738

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds. © 2023 IEEE.

关键词： Edge computing

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A Composite Schema Matching Method Based on Contrastive Learning in Data Integration

A Composite Schema Matching Method Based on Contrastive Lear...

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International Conference on Computer Supported Cooperative Work in Design

作者： Liying Shao Xinyu Hao Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Hohhot China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Schema matching is a crucial step in data integration for in-depth information mining, aimed at capturing seman-tic correspondences between elements of multi-source datasets. The existing schema matching methods still face problems such as insufficient representations of schema information diversity, poor cross-domain expression ability, and inadequate adaptability to complex data environments. We propose a composite schema matching method based on contrastive learning. Our method de-signs multi-domain word lists for data augmentation, the schema-based matching part fine-tunes pre-trained models through con-trastive learning and finally designs multi-model voting strategies to complete the composite matches. It also designs a format validation strategy to enhance the performance of the schema matching model. The experimental results show that our method has improved the F1 comprehensive performance indicators on three benchmark datasets by an average compared to existing benchmark methods.

关键词： Adaptation models Federated learning Face recognition Semantics Data integration Contrastive learning Benchmark testing Data augmentation Data models Data mining

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A Workflow for the Continuous Deployment of Quantum Services

A Workflow for the Continuous Deployment of Quantum Services

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software Services engineering (SSE) IEEE International Conference on

作者： Javier Romero-Alvarez Jaime Alvarado-Valiente Enrique Moguel Jose Garcia-Alonso Juan M. Murillo Quercus Software Engineering Group Universidad de Extremadura Cáceres Spain Computing and Advanced Technologies Foundation of Extremadura Cáceres Spain

The rapid advancements in quantum computing have opened new possibilities for solving complex problems in various fields, including cryptography, optimization, and simulation. However, the current approaches to quantum computing often require a deep understanding of quantum hardware and low-level programming languages, making it difficult for software developers to create and deploy quantum services. This paper argues for the adaptation of Service-Oriented computing principles to quantum computing, enabling a level of abstraction from hardware that allows developers to focus on application development. This will allow developers to create and deploy quantum services, similarly to how Service-Oriented computing has made it easier to develop traditional software services. This paper presents a Continuous Deployment approach for quantum service development, which involves the generation and deployment of quantum services. To do that, we propose an extension of the OpenAPI specification to generate services implementing a quantum algorithm. To validate the feasibility of the proposed process, we use a variety of quantum algorithm implementations to test their generation and deployment of quantum services.

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